Phospho-heavy-labeled-spiketide FAIMS stepped-CV DDA (pHASED) provides real-time phosphoproteomics data to aid in cancer drug selection

Global high-throughput phosphoproteomic profiling is increasingly being applied to cancer specimens to identify the oncogenic signaling cascades responsible for promoting disease initiation and disease progression; pathways that are often invisible to genomics analysis. Hence, phosphoproteomic profiling has enormous potential to inform and improve individualized anti-cancer treatment strategies. However, to achieve the adequate phosphoproteomic depth and coverage necessary to identify the activated, and hence, targetable kinases responsible for driving oncogenic signaling pathways, affinity phosphopeptide enrichment techniques are required and often coupled with offline high-pressure liquid chromatographic (HPLC) separation prior to nanoflow liquid chromatography–tandem mass spectrometry (nLC-MS/MS). These complex and time-consuming procedures, limit the utility of phosphoproteomics for the analysis of individual cancer patient specimens in real-time, and restrict phosphoproteomics to specialized laboratories often outside of the clinical setting. To address these limitations, here we have optimized a new protocol, phospho-heavy-labeled-spiketide FAIMS Stepped-CV DDA (pHASED), that employs online phosphoproteome deconvolution using high-field asymmetric waveform ion mobility spectrometry (FAIMS) and internal phosphopeptide standards to provide accurate label-free quantitation (LFQ) data in real-time. Compared with traditional single-shot LFQ phosphoproteomics workflows, pHASED provided increased phosphoproteomic depth and coverage (phosphopeptides = 4617 pHASED, 2789 LFQ), whilst eliminating the variability associated with offline prefractionation. pHASED was optimized using tyrosine kinase inhibitor (sorafenib) resistant isogenic FLT3-mutant acute myeloid leukemia (AML) cell line models. Bioinformatic analysis identified differential activation of the serine/threonine protein kinase ataxia-telangiectasia mutated (ATM) pathway, responsible for sensing and repairing DNA damage in sorafenib-resistant AML cell line models, thereby uncovering a potential therapeutic opportunity. Herein, we have optimized a rapid, reproducible, and flexible protocol for the characterization of complex cancer phosphoproteomes in real-time, a step towards the implementation of phosphoproteomics in the clinic to aid in the selection of anti-cancer therapies for patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12014-022-09385-7.


Introduction
Mass spectrometry based approaches for global highthroughput quantitation of cellular phosphoproteomes have been increasingly applied to cancer specimens as they provide powerful tools for the identification of signaling pathways including kinases, phosphatases, tumor suppressor and cell cycle regulators that drive disease initiation and progression [1][2][3][4]. Deregulation of kinase activity plays a critical role in cancer development and relapse [5][6][7][8], highlighting kinases as important therapeutic targets in the clinic [9,10]. This is particularly the case for FLT3 kinase-driven acute myeloid leukemia (AML) patients. The FMS-like tyrosine kinase 3 (FLT3) receptor tyrosine kinase is recurrently mutated in AML and is the target for FLT3 inhibitors. The most common mutations are internal tandem duplications (ITD) and kinase domain mutations (e.g., D835). Resistance to FLT3 inhibitors is often associated with the emergence of dual FLT3-ITD/D835 mutations, however the pathways upregulated during the development of resistance are yet to be fully characterized [3,6,7]. Therefore, phosphoproteomic profiling of patients' specimens in real-time to identify the activated kinases responsible for driving downstream oncogenic signaling cascades, provides us with the opportunity to repurpose clinically relevant therapeutics [11][12][13][14][15], and thus aid in the development of personalized treatment strategies that may improve overall survival.
Several methods have been developed for the quantitative characterization of phosphoproteins in complex biological samples using shotgun proteomics [16][17][18][19]. Stable isotope-labeling strategies such as tandem mass tag (TMT) approaches have become increasingly popular due to the capability to multiplex analysis of up to 18 complex matrices simultaneously [20]. TMT protocols enable samples to be pooled prior to nano liquid chromatography-tandem mass spectrometry (nLC-MS/MS), saving instrument time and reducing technical variations in the workflow.
However, the high cost of reagents, fixed number of samples, and sample preparation time and complexity combine to limit the utility of TMT protocols for the impromptu assessment of patient specimens in real-time and hence their broad uptake in the clinic. Label-free quantitation (LFQ) strategies provide quantitative phosphoproteomic data without the use of isotopic-tags, mainly through the direct inference of protein abundance using the measured intensity of detected peptides, or indirect inference based on the number of phosphopeptide-spectrum matches (PSMs) obtained for each protein [21]. LFQ protocols have the capacity to overcome some of the TMT-workflow limitations by reducing the complexity of sample preparation, saving both time and money on costly reagents. Additionally, there is no limit to the number of matrices that can be analyzed, enabling the comparison of larger sets of samples than when using label-based approaches. Such strategies therefore hold obvious appeal in the context of highly aggressive forms of cancer in which the design of appropriate treatment strategies is time-sensitive, and hence the ability to rapidly and reproducibly perform phosphoproteomic profiling is of critical importance [4]. However, label-free strategies have their own limitations, which include the inherent variability of individual sample preparation and loading, and the requisite number of replicates. Additionally, chromatographic conditions and the semirandom nature of data acquisition have also an impact in sample reproducibility [22]. The addition (spike-in) of known concentrations of standard heavy-labeled exogenous phosphopeptides for sample normalization, can help to overcome some of these limitations [17,23], and therefore provides a strategy to normalize protein expression and phosphorylation abundance from different cancer specimens analyzed at any time.
The highly complex nature of cancer phosphoproteomes necessitates that sample preparation protocols are coupled with phosphopeptide enrichment and sample pre-fractionation prior to nLC-MS/MS analysis to achieve adequate phosphoproteome resolution. By interfacing phosphopeptide enrichment [12,14,24] with separation via High-field asymmetric waveform ion mobility spectrometry (FAIMS) [25,26] prior to high-resolution mass spectrometry (MS), the collection of single-shot proteomic data is possible without the need to perform conventional two-dimensional liquid chromatography (2D-LC) approaches, and provides deep phosphoproteomic coverage to identify cancer-associated drug targets, in real-time. In seeking to combine the salient features of these analytical modalities, here we report the optimization of a new protocol that employs online phosphoproteome deconvolution in tandem with LFQ in the presence of internal control heavy-labeled standards.
This protocol was optimized to identify kinases driving disease progression and therapy resistance in real-time.
To determine the pre-clinical utility of this approach, pHASED was applied to isogenic FLT3-mutant AML cell lines resistant to the tyrosine kinase inhibitor sorafenib.

Sample preparation and protein extraction
Snap frozen transduced FDC-P1 cells expressing AML associated FLT3-mutations were lysed in 100 μL of icecold 0.1 M Na 2 CO 3 , pH 11.3 containing protease and phosphatase inhibitors (Sigma, cat. #P8340-5ML, and #4906837001 respectively), by sonication (2 × 20 s cycles, 100% output power) (as described [11,24,27]). Protein concentration was determined using a Bicinchoninic acid (BCA) protein estimation assay, as per manufacturer's instructions (Thermo Fisher Scientific). Protein samples were then diluted in 6 M Urea/2 M Thiourea and reduced using 10 mM dithiothreitol (DTT) by incubation for 30 min at room temperature (RT). Reduced cysteine residues were then alkylated using 20 mM iodoacetamide by incubation for 30 min at RT in the dark. Enzymatic digestion was achieved using Trypsin/Lys-C mixture (Promega) at an enzyme-to-substrate ratio of 1:50 (w/w) and incubated for 3 h at RT. Triethylammonium bicarbonate (TEAB, 50 mM, pH 7.8) was then added to dilute urea concentration below 1 M, and samples were incubated overnight at RT. Lipid precipitation was performed using formic acid and trichloroacetic acid (TCA). Briefly, a final concentration of 2% formic acid was added to each sample, prior to centrifugation at 14,000×g for 10 min. Remaining lipopeptides were then precipitated with 20% (w/w) TCA and incubated on ice for at least 1 h prior to centrifugation. Pellets were washed with ice cold 0.01 M hydrochloric acid (HCl)/90% acetone and supernatants containing peptides were combined. Peptides were desalted using Oasis HLB solid phase extraction (SPE) cartridges and a VisiprepTM SPE Vacuum Manifold (12-port model; Sigma). The SPE cartridges were activated using 100% acetonitrile (ACN), equilibrated using 0.1% trifluoroacetic acid (TFA), and blocked with 33 µg of trypsin-digested bovine serum albumin (BSA) peptides prior to sample clean-up. Acidified samples (pH < 3) were loaded onto SPE cartridges with liquid passed through the solid phase dropwise using vacuum pressure. The cartridges were washed with 0.1% TFA followed by sequential elution of peptides using 60% ACN/0.1% TFA and 80% ACN/0.1% TFA. Eluted peptides were then resuspended in TEAB (50 mM, pH 8) and quantified using a Qubit 2.0 Fluorometer, as per manufacturer's instructions (Thermo Fisher Scientific). A total of 200 µg of peptide per sample was utilized for TiO 2 enrichment. Spike-in heavy-labeled phosphorylated peptides (Additional file 2: Table S1; including individually tyrosine, threonine or serine phosphorylated heavy-labeled spiketides, 8 fmol/200 μg of sample) were added as internal controls. Phosphopeptide enrichment was modified based on previous protocols [12,14,19]. In brief, each peptide sample was suspended in 80% ACN, 5% TFA, and 1 M glycolic acid (loading buffer). TiO 2 beads were added at 0.6 mg per 100 µg peptide (w/w), and samples were mixed at RT for 15 min. The supernatant was incubated with half the amount of fresh TiO 2 beads, and resultant supernatants containing non-phosphorylated peptides (non-modified = NM fraction) were removed and stored. The two sets of beads with bound phosphopeptides were pooled using 100 μL of loading buffer, followed by sequential washing with 80% ACN/1% TFA, and 10% ACN/0.1% TFA. Phosphopeptides were eluted with 28% ammonia hydroxide solution (1% v/v, pH 11.3) then passed through a C8 stage tip to remove residual beads [19]. Phosphopeptides were lyophilized completely prior to resuspension in 2% ACN/0.1% TFA for nLC-MS/ MS analysis.

Nanoflow liquid chromatography-tandem mass spectrometry (nLC-MS/MS)
Reverse phase nanoflow LC-MS/MS was performed using a Dionex Ultimate 3000RSLC nanoflow high-performance liquid chromatography system coupled with an Orbitrap Exploris 480 MS equipped with or without a front-end FAIMS Interface (Thermo Fisher Scientific) for pHASED or single-shot injection, respectively. Approximately 700 ng of phosphopeptide (single-shot injection, or per CV in pHASED) were loaded onto an Acclaim PepMap 100 C18 75 μm × 20 mm trap column for preconcentration and online de-salting. The same chromatographic conditions were used in both experiments. Separation was achieved using an EASY-Spray PepMap C18 75 μm × 25 cm, employing a gradient of 0-35% solvent B (solvent A = 0.1% formic acid, solvent B = 90% ACN, 0.1% formic acid) at a flow rate of 250 nL/min over 75 min. For pHASED, the mass spectrometer was operated in positive mode with the FAIMS Pro interface. Four compensation voltages (CV; − 70, − 60, − 50, − 40) were individually run for each biological triplicate. Full MS/ data dependent acquisition (DDA) was performed using the following parameters: Orbitrap mass analyzer set at a resolution of 60,000, to acquire full MS with an m/z range of 350-1200, incorporating a standard automatic gain control target of 1e 6 and maximum injection time of 50 ms. The 20 most intense multiply charged precursors were selected for higher-energy HCD with a collisional energy of 30. MS/MS fragments were measured at an Orbitrap resolution of 15,000 incorporating a normalized automatic gain control target of 250% and a maximum injection time of 120 ms.

Data processing and bioinformatic analysis
Data analysis was performed using Proteome Discoverer 2.4 (Thermo Fisher Scientific). Sequest HT was used to search against UniProt Mus musculus database (17,091 sequences, downloaded 25/04/2022) and Homo sapiens FLT3 FASTA file containing WT and mutant FLT3 sequences (3 sequences, downloaded 21/02/20). Database searching parameters included up to two missed cleavages, precursor mass tolerance set to 10 ppm and fragment mass tolerance of 0.02 Da. Cysteine carbamidomethylation was set as a fixed modification while dynamic modifications included phosphorylation (S, T, Y), and heavy-labeled 13C(6)15N(2) (K), and 13C(6)15N(4) (R) to identify spiked-in heavy-labeled phospho-spiketides. Interrogation of the database was performed to evaluate the false discovery rate (FDR) of peptide identification based on q-values estimated from the target-decoy search approach using Percolator. An FDR rate of 1% was set at the peptide level to filter out target peptide spectrum matches over the decoy-peptide spectrum matches. To account for variations in sample injection, reporter ion abundances were normalized to the spiked-in heavy-labeled phosphopeptides included as a FASTA file (Additional file 1: Fig. S1). For quantification and comparison, each ratio was transformed to log 2 scale (log 2 ratio).

Experimental design and statistical rationale
Phosphoproteomic data analysis was performed using three FDC-P1 isogenic cell lines (n = 3 biological replicates). Four compensation voltages (CV; − 70, − 60, − 50, − 40) were individually analyzed for each biological replicate for pHASED. Differentially expressed phosphopeptides and phosphorylation sites were defined as those with a significant (p ≤ 0.05) log 2 fold change ≥ 0.5 or ≤ − 0.5. Differences between sample groups were analyzed by unpaired Student's t-tests or one-way ANOVA and considered significant when p ≤ 0.05. Graphical data was analyzed and prepared using Perseus (1.6.2.2), String (11.5), and GraphPad Prism (9.0.1). Results are presented as mean values ± SEM.

Ingenuity pathway analysis
Ingenuity Pathway Analysis software (IPA; Qiagen) was used to analyze each phosphoproteomic dataset (as previously described [12,14]). Canonical pathways, and upstream regulator analyses were generated and assessed based on p-value.

Cell growth assay
Cell growth and viability were determined by trypan blue exclusion. Cells were seeded at 1e 5 cells per mL. Cell number and viability were determined at 0 h, 24 h, and 48 h timepoints by trypan blue exclusion (n = 3 independent biological replicates). Doubling time was calculated using the following formula [27], and statistical significance determined via ordinary one-way ANOVA:

Cytotoxicity assays
Cell lines were treated with the FLT3 inhibitor sorafenib (Selleckchem) [31], and ATM inhibitor KU-60019 (Selleckchem) [32] either alone or in combination. Cells were seeded into 96 well plates at 2e 4 cells per well, and viability following treatments was measured using Resazurin (excitation 544 nm, emission 590 nm; 0.6 mM Resazurin, 78 μM Methylene Blue, 1 mM potassium hexacyanoferrate (III), 1 mM potassium hexacyanoferrate (II) trihydrate (Sigma), dissolved in sterile phosphate buffered saline). Synergy of dose-response and combined effect of the two drugs were assessed using the method of Bliss independence model [33].

pHASED provided improved phosphopeptide quantification compared to the LFQ workflow
Phosphopeptide enrichment using TiO 2 [12,13,19,24], coupled with LFQ using 'pHASED' described herein, employs heavy-labeled internal phospho-spiketides and FAIMS interface [25,26], to increase phosphoproteome deconvolution and coverage for the analysis of samples in real-time (Fig. 1). To simulate the limited amount of starting material that is often provided by clinical teams following a cancer patient's biopsy, resection, or bone marrow aspiration, we used a maximum of 200 µg of starting material. We performed initial comparison of our optimized pHASED protocol with traditional singleshot LFQ phosphoproteomics using three isogenic cell line models of FLT3-mutant AML in biological triplicate ( Table 1; n = 9 samples/experiment). The sample preparation in pHASED includes the addition of spike-in of heavy-labeled phospho-spiketides of known concentration to normalize sample injection and phosphopeptide quantitation (Additional file 1: Fig. S1). To increase phosphoproteome coverage, we employed online deconvolution using FAIMS interface employing external stepping of four different compensation voltages (CV; − 70, − 60, − 50, − 40) over a 75 min gradient. Individual sample injection per CV provided more in-depth coverage (Fig. 1).
pHASED analysis of isogenic AML cell lines identified increased numbers of PSMs at lower CVs ( Fig. 2A,   B), with the greatest number of unique PSMs identified at − 60 V ( Fig. 2A, B, Additional file 2: Table S2) [34]. Similar PSM distributions were seen across CVs as previously reported for studies employing FAIMS to characterize the proteome [26] and phosphoproteome [25] (Fig. 2A,  B). Analysis of PSMs identified that lower CVs tended to harbor lower m/z (Fig. 2C), across charge states (Fig. 2D).
To examine the reproducibility of the phosphopeptide quantification achieved using pHASED we performed Pearson Correlation analysis of the biological replicates (n = 3) across each cell line (n = 3) (Fig. 3). Correlation was performed by plotting normalized phosphopeptide abundances from each biological replicate per sample in a correlation matrix. These analyses confirm the fidelity and reproducibility of pHASED across samples and biological replicates (Fig. 3A-D) (average r = 0.84 ± 0.03).

pHASED identified relevant therapeutic drug targets in drug resistant AML
Importantly, pHASED identified 2.4× more phosphoproteins (1% FDR) with predicted kinase activity than singleshot LFQ (mean = 51.33 for LFQ; 123.3 for pHASED; p = 0.0002) (Fig. 4D, Additional file 2: Tables S6, S7). Like many other cancers, dysregulated kinase activity is a main feature driving AML oncogenic signaling, with the most common mutations seen at diagnosis in the and Ingenuity Pathway Analysis (IPA) was employed using the phosphorylation profile identified by both methods (Additional file 2: Tables S9-S12, Fig. 5). FLT3-ITD mutations are seen in approximately 27% of AML patients at diagnosis and are associated with a high risk of relapse [3]. Resistance to commonly used tyrosine kinase inhibitors (TKIs) including sorafenib (Additional file 2: Table S8) in FLT3-ITD + AML patients, commonly occurs following the acquisition of a secondary point mutation at aspartic acid 835 in the kinase domain of FLT3-ITD (FLT3-ITD/D835V and FLT3-ITD/D835Y, henceforth referred to as "double mutant") [37,38].

pHASED identified DNA damage and repair pathways associated with sorafenib resistance in FLT3-mutant AML
In accordance with KSEA predictions, IPA of the phosphoproteomes of resistant cells (Fig. 5D, Additional file 2: Table S12) identified cell cycle regulation (Cell cycle control of Chromosomal Replication, p < 0.001), and DNA damage and repair ATM signaling (p < 0.001) as the most significantly enriched pathways. Both mutants were also enriched for FLT3, and AML associated signaling pathways such as ERK/MAPK, JAK/STAT, mTOR, and PI3K/AKT, although with less power (Additional file 2: Table S12).
The Serine/threonine protein kinase (ATM) regulates response to DNA damage caused by double-strand breaks (DSBs) [14]. ATM is a member of the phosphoinositide 3-kinase (PI3K)-related protein kinase (PIKK) family, and signals through DNA damage response kinases ATR, DNA-PKcs and Nonsense Mediated MRNA Decay Associated PI3K Related Kinase (SMG1) [51,52]. One potent mechanism of increased DSBs is via the excess production of reactive oxygen species (ROS) [53,54]. Increased ROS production by the NADPH oxidase (NOX) family of enzymes in acute leukemias, particularly FLT3-ITD AML, has been increasingly studied over the last few years, and highlights that elevated ROS is a mechanism conferring survival advantages in FLT3mutant AML [53][54][55][56]. Given ATM signaling was predicted to be one of the top ranked canonical pathways driving the DNA damage repair and response pathways in resistant cells (Fig. 5D), we chose to analyze this pathway to test the biological utility of the phosphoproteomic analysis generated via pHASED in FLT3-ITD/D835V and FLT3-ITD/D835Y mutant cells.

Discussion
Proteomics and phosphoproteomics have been acknowledged as being among the most effective strategies to predict drug sensitivities [1,57]. However, we are yet to establish phosphoproteomic profiling in the clinical setting, or even to provide an additional resource to genomically predicted therapeutic strategies; the establishment of which would represent a pivotal advance in precision-medicine treatment regimens. Clinical phosphoproteomic profiling has enormous potential to identify treatment targets for cancers that harbor untargetable mutations like seen in uniformly lethal pediatric high-grade gliomas [1,4,58], or that are invisible to genomics approaches like those described herein, or phosphoproteomes to be used as an indicator of prognosis in the de novo and refractory settings, in real-time. Indeed, the optimization of pHASED reported herein, goes some way to moving phosphoproteomics from the discovery laboratory to that of the well-equipped pathologist. The reduced sample preparation complexity coupled with the deep phosphoproteomic coverage possible with pHASED, provides users with the capacity to prepare, fractionate, and sequence the phosphoproteome of any biological system in less than a week. Due to the combination of accurate LFQ and online deconvolution using FAIMS, pHASED maintained deep phosphoproteomic coverage without the need for offline 2D-LC techniques. Compared to single-shot analysis, pHASED increased LC-MS/MS run time by 1.8 days (45 h for pHASED; 11.25 h for LFQ); a limitation compensated by the increased phosphoproteomic depth of coverage, and the identification of a more targetable kinases. Furthermore, in our attempt to establish a robust, reproducible platform for the prediction of novel therapeutic targets in clinically relevant timeframes for AML patients resistant to therapies, we also used unnormalized pHASED phosphoproteomic data, as the phosphorylation of key DNA repair pathway proteins identified herein were not influenced by the abundance of the respective protein.
FAIMS was initially and elegantly optimized to provide single-shot LC-MS/MS results that compared favorably with 2D-LC fractionation experiments [26]. Specifically, FAIMS was previously reported in the context of analyzing the global phosphoproteome of a cell line. MS/ MS analyses using FAIMS, identified 15% more unique phosphosites compared to single-shot analysis without FAIMS. Accordingly, our pHASED workflow using isogenic models of AML, identified a 25% increase in the number of unique phosphosites identified compared to single-shot analysis. Furthermore, pHASED also identified 50% more multiphosphorylated peptides compared to single-shot analysis without FAIMS, similar to the increase identified in previous studies using FAIMS, including the analysis of different sample types [25].
Analyzing the signaling pathways identified by both MS approaches provided molecular insights to help dissect therapeutic vulnerabilities. Indeed, pHASED identified considerably more deregulated kinases and canonical pathways compared to single-shot analysis, with IPA predicting increased ATM signaling in both FLT3-ITD/D835V and FLT3-ITD/D835Y double mutant cell lines compared to FLT3-ITD mutations alone. ATM plays a functional role in the cellular response to DNA DSBs. Here it protects the cell against genotoxic stress, but, in cancer cells, helps to drive resistance to anticancer therapies thus favoring leukemic growth and survival [59]. Therefore, it is unsurprising that ATM signaling may play a role in resistance to sorafenib in cells harboring double mutant FLT3-ITD/D835. However, sorafenib is not only a potent inhibitor of wt-FLT3 and FLT3-ITD, but also inhibits other receptor tyrosine kinases including VEGFR, PDGFR, KIT and RET, as well as downstream serine/threonine kinases including RAF/MEK/ERK [31]. In glioma cells, ATM inhibitors increased radiotherapy sensitivity [32,60,61], with ATM signaling through the RAF/MEK/ERK pathway critical for radiation-induced ATM activation, suggestive of a regulatory feedback loop between ERK and ATM [62]. Indeed, this feedback loop may help to explain the 48-fold increase in IC 50 seen between the cell types. Sorafenib dose-dependently induced the generation of ROS in tumor cells in vitro and in vivo [63], and hence it is highly possible that in FLT3-ITD/D835 double mutants, RAF/MEK/ERK signaling through ATM helps to maintain proliferation and promote DNA repair, even under situations of genotoxic stress induced by high dose sorafenib.
pHASED identified more unique phosphorylation changes in ATM substrates in FLT3-ITD/D835Y cells compared to FLT3-ITD/D835V cells (log 2 ≥ 0.5) (Additional file 2: Table S16). Combination cytotoxicity assays revealed significantly increased synergy between sorafenib and the ATM inhibitor KU-60019 at physiologically relevant doses (most strikingly and unsurprisingly in FLT3-ITD/D835Y cells) thus providing a treatment paradigm for patients harboring sorafenib resistance. It is interesting to note that the double mutant FLT3-ITD/D835Y showed decreased ATR activity by KSEA, whereas FLT3-ITD/D835V cells showed predictions of ATR activation. The difference in sensitivity may result from the cross-regulation seen between ATM and ATR at different stages of DDR [64]. The increased phosphosite coverage resulting from pHASED analyzes provides a more accurate indication of the regulation of the ATM signaling pathway, and hence highlights mechanisms promoting resistance to sorafenib [13]; information that can be exploited to tailor effective preclinical treatment strategies.
Although pHASED may afford the opportunity to perform an unrestricted number of analyzes (of benefit in the clinical setting where cancer diagnosis never follows a predictable schedule), there remains important questions about how phosphoproteomics would be practically implemented as a clinical decision-making tool. For example, consideration needs to be given to sample processing time and the methods of patient sample collection; the phosphoproteome of leukemic blasts isolated from the bone marrow will differ from those sequenced from leukemic blasts isolated from peripheral blood. Additionally, the steps taken to enrich leukemic blasts following bone marrow trephine biopsy or phlebotomy are to be considered as alterations in signaling pathway activity can be influenced simply by the culture media used, or even the type of blood tube used at the time of sample collection [27], necessitating optimization and standardization of workflows. Importantly, for phosphoproteomics to aid in the selection of treatments for cancer patients, the assessment of which pathways should be targeted and by which drugs needs to be evaluated under clinical trial conditions, like those testing whole genome sequencing (WGS) and RNA sequencing (RNAseq) strategies, in order to ensure robust recommendations can be made based on phosphoproteomic data generated via pHASED [65].
In summary, the data generated here provides an optimized method for LFQ and high-throughput phosphoproteomic data generation from limited amounts of starting material that maintains deep phosphoproteomic coverage without the need for complex 2D-LC strategies. pHASED provides the flexibility to analyze samples as they present and is not limited by the number of analyzes that can be performed. Reduced time and sample preparation complexity, and the optimization of online phosphoproteome deconvolution using a stepped CV FAIMS interface, provides an accurate tool for the reproducible characterization of complex cancer cell phosphoproteomes in less than a week. Moreover, pHASED successfully identified novel drug targets and potential therapeutic strategies to treat AML models resistant to therapies used in the clinic. We hope this optimized technology will help in the rapid characterization of other highly aggressive forms of cancer, an important step towards improving treatment outcomes for cancer sufferers.
Additional file 1. Additional materials and methods. Figure S1. Standard spike-in control abundances (n = 3 biological replicates). Abundances for heavy-labeled spike-in peptides phosphorylated at tyrosine (Y), threonine (T), and serine (S) residues for FLT3-mutant cell lines. A Raw and B normalized peptide abundances of spike-in controls according to different FLT3 cell lines. Total SBDS C raw and D normalized protein abundances for FLT3-ITD, FLT3-ITD/D835V, and FLT3-ITD/D835Y cell lines.
Additional file 3: Table S1. SBDS heavy-labeled phosphorylated peptide standards. Table S2. Common and unique phosphoproteins identified across all four CVs based on PSM acquisition. Table S3. High confidence modification sites identified in LFQ (p < 0.01). Table S4. High confidence modification sites identified in pHASED (p < 0.01). Table S5. Unique and common phosphoproteins identified in LFQ and pHASED datasets. Table S6. Phosphorylated master protein kinases identified in LFQ dataset (p < 0.01). Table S7. Phosphorylated master protein kinases identified in pHASED dataset (p < 0.01). Table S8. FLT3-D835 mutations associated with resistance to tyrosine kinase FLT3 inhibitors. Table S9. Kinase-Substrate analysis of LFQ dataset for resistant cells in comparison to FLT3-ITD (log2 fold change ± 0.5). Table S10. Kinase-Substrate analysis of pHASED dataset for resistant cells in comparison to FLT3-ITD (log2 fold change ± 0.5). Table S11. Canonical pathways identified as significantly associated with LFQ dataset for resistant cells in comparison to FLT3-ITD. Table S12. Canonical pathways identified as significantly associated with pHASED dataset for resistant cells in comparison to FLT3-ITD. Table S13. Kinase activity inferred by KSEA analysis of phosphorylation changes in pHASED dataset (log2 ± 0.5, p ≤ 0.05) for resistant cells in comparison to FLT3-ITD. Table S14. Mutation-specific response to sorafenib. IC50 compared to FLT3-ITD. Table S15. Bliss Synergy scores for sorafenib in combination with KU-60019 at different doses. Table S16. Unique ATM substrates identified with increased phosphorylation (log2 ≥ 0.5) in pHASED dataset for resistant cells in comparison to FLT3-ITD. Table S17. Vector mutations in FLT3 gene.